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Graph component labelling, which is a subset of the general graph colouring problem, is a computationally expensive operation that is of importance in many applications and simulations. A number of data-parallel algorithmic variations to the component labelling problem are possible and we explore their use with general purpose graphical processing units(More)
Recent developments in processing devices such as graph-ical processing units and multi-core systems offer opportunities to make use of parallel techniques at the chip level to obtain high performance. We discuss the difficulties in establishing suitable benchmark codes for making comparisons across these device architectures and in a way that is(More)
Graphical Processing Units (GPUs) have recently attracted attention for scientific applications such as particle simulations. This is partially driven by low commodity pricing of GPUs but also by recent toolkit and library developments that make them more accessible to scientific programmers. We report on two further application paradigms – regular mesh(More)
The Cahn-Hilliard-Cook equation continues to be a useful model describing binary phase separation in systems such as alloys and other physical and chemical applications. We describe our investigation of this field equation and report on the various discretisation schemes we used to integrate the system in one-, two-and three-dimensions. We also discuss how(More)
SUMMARY Graphical Processing Units (GPUs) are good data-parallel performance accelerators for solving regular mesh partial differential equations (PDEs) whereby low-latency communications and high compute to communications ratios can yield very high levels of computational efficiency. Finite-difference time-domain methods still play an important role for(More)
Computational scientific simulations have long used parallel computers to increase their performance. Recently graphics cards have been utilised to provide this functionality. GPGPU APIs such as NVidia's CUDA can be used to harness the power of GPUs for purposes other than computer graphics. GPUs are designed for processing two-dimensional data. In previous(More)
Data-parallel accelerator devices such as Graphical Processing Units (GPUs) are providing dramatic performance improvements over even multicore CPUs for lattice-oriented applications in computational physics. Models such as the Ising and Potts models continue to play a role in investigating phase transitions on small-world and scale-free graph structures.(More)
Purpose Graphical Processing Units (GPGPU). We discuss: general memory layouts; specific optimisations possible for dimensions that are powers-of-two; and common transformations such as inverting, shifting and crinkling. We present performance data for some illustrative scientific applications of these layouts and transforms using several current GPU(More)
The Cahn-Hilliard-Cook equation continues to be a useful model describing binary phase separation in systems such as alloys and other physical and chemical applications. We describe our investigation of this field equation and report on the various discretisation schemes we used to integrate the system in one-, two-and three-dimensions. We also discuss how(More)